README.md

nlp-preprocessing

nlp-preprocessing provides text preprocessing functions i.e. text cleaning, dataset preprocessing, tokenization etc

Installation

pip install nlp_preprocessing

Tutorial

1. Text Cleaning

from nlp_preprocessing import clean

texts = ["Hi I am's nakdur"]
cleaned_texts = clean.clean_v1(texts)

There are multiple cleaning functions:

data_list = to_lower(data_list)
data_list = to_normalize(data_list)
data_list = remove_href(data_list)
data_list = remove_control_char(data_list)
data_list = remove_duplicate(data_list)
data_list = remove_underscore(data_list)
data_list = seperate_spam_chars(data_list)
data_list = seperate_brakets_quotes(data_list)
data_list = break_short_words(data_list)
data_list = break_long_words(data_list)
data_list = remove_ending_underscore(data_list)
data_list = remove_starting_underscore(data_list)
data_list = seperate_end_word_punctuations(data_list)
data_list = seperate_start_word_punctuations(data_list)
data_list = clean_contractions(data_list)
data_list = remove_s(data_list)
data_list = isolate_numbers(data_list)
data_list = regex_split_word(data_list)
data_list = leet_clean(data_list)
data_list = clean_open_holded_words(data_list)
data_list = clean_multiple_form(data_list)

2. Dataset Prepration

from nlp_preprocessing import dataset as ds
import pandas as pd

text = ['I am Test 1','I am Test 2']
label = ['A','B']
aspect = ['C','D']
data = pd.DataFrame({'text':text*5,'label':label*5,'aspect':aspect*5})
data

data_config = {
            'data_class':'multi-label',
            'x_columns':['text'],
            'y_columns':['label','aspect'],
            'one_hot_encoded_columns':[],
            'label_encoded_columns':['label','aspect'],
            'data':data,
            'split_ratio':0.1
          }

dataset = ds.Dataset(data_config)
train, test = dataset.get_train_test_data()

print(train['Y_train'],train['X_train'])
print(test['Y_test'],test['X_test'])
print(dataset.data_config)

3. Seq token generator

texts = ['I am Test 2', 'I am Test 1', 'I am Test 1', 'I am Test 1','I am Test 1', 'I am Test 2', 'I am Test 1', 'I am Test 2','I am Test 2']

tokens = seq_gen.get_word_sequences(texts)
print(tokens)

4. Token embedding creator

from nlp_preprocessing import token_embedding_creator

vector_file='../input/fasttext-crawl-300d-2m-with-subword/crawl-300d-2m-subword/crawl-300d-2M-subword.vec'
input_file='../input/complete-tweet-sentiment-extraction-data/tweet_dataset.csv'
column_name='text'

processor = token_embedding_creator.Processor(vector_file, input_file, column_name)
output_dir = '.'
special_tokens = ['[UNK]','[SEP]']

processor.process(output_dir, special_tokens)

#Loading vectors from  ../input/fasttext-crawl-300d-2m-with-subword/crawl-300d-2m-subword/crawl-300d-2M-subword.vec  type:  index
#Writing vocab at  ./full_vocab.txt
#1%|          | 218/40000 [00:00<00:18, 2176.72it/s]
#Generating unique tokens ...
#100%|██████████| 40000/40000 [00:18<00:00, 2180.53it/s]
#Writing vocab at  ./vocab.txt
#Loading vectors from  ../input/fasttext-crawl-300d-2m-with-subword/crawl-300d-2m-subword/crawl-300d-2M-subword.vec  type:  embedding
#Writing vocab at  ./vocab.txt
#Making Final Embedding ...
#Writing embedding at  ./embeddings.npy
#Processing Done !
#Vocab stored at : ./vocab.txt  of size:  25475
#Embedding stored at : ./embeddings.npy of shape:  (25475, 300)

5. seq_parser_token_generator

from nlp_preprocessing import seq_parser_token_generator

text = ['hi how are you']

pos_token, tag_token, dep_token = get_tokens(text[0])

pos_tokens, tag_tokens, dep_tokens = get_tokens_plus(text, 120)